How Alignment Routes: Localizing, Scaling, and Controlling Policy Circuits in Language Models
arXiv:2604.04385v5 Announce Type: replace-cross Abstract: We localize the policy routing mechanism in alignment-trained language models. An intermediate-layer attention gate reads detected content and triggers deeper amplifier heads that boost the signal toward refusal. In smaller models the gate and amplifier are single heads; at larger scale they become bands of heads across adjacent layers. The gate contributes under 1% of output DLA, yet interchange testing (p = 120 detects the same motif in twelve models from six labs (2B to 72B), though specific heads differ by lab. Per-head ablation wea
This research provides a localized understanding of how alignment mechanisms function within large language models, a critical step as these models become more autonomous and broadly deployed.
A strategic reader should care because understanding and controlling these policy circuits is fundamental to developing safe, reliable, and steerable AI, which directly impacts trust, deployment, and regulatory frameworks.
The ability to localize, scale, and control specific 'refusal' circuits within language models changes how researchers can engineer alignment, moving from black-box approaches to more mechanistic interpretability.
- · AI Safety Researchers
- · Open-source AI Developers
- · Regulatory Bodies
- · AI Model End-Users
- · Developers of Uninterpretable AI Systems
- · Black-box AI Alignment Approaches
Researchers gain precise control over specific model behaviors like refusal, enhancing safety and steerability.
This detailed interpretability could accelerate the development of more robust alignment techniques and foster greater public trust in advanced AI systems.
Improved control over AI behavior may influence future regulations, shifting focus from outcomes to mechanistic interpretability and auditability.
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